In a recent survey conducted by NewVantage Partners, it was revealed that while 93.9% of executives anticipate increasing their data investments in 2023, only 23.9% of organizations consider themselves truly data-driven. This disconnection raises questions about where these investments are directed and what obstacles hinder executives from realizing their vision of a data-driven future for their companies.
The impact of data literacy on organizational success
The primary hurdle, as identified by 79% of these executives, is cultural issues within organizations. This underscores the crucial role of people in driving or impeding the transition to a data-driven approach. It’s evident that data alone cannot transform a business; it’s the people who animate it.
Over a decade ago, Gartner analyst Svetlana Sicular highlighted two fundamental truths about big data that are often overlooked. First, organizations already possess individuals who understand their data better than mystical data scientists. Second, learning complex data technologies like Hadoop is often easier than grasping the intricacies of a company’s unique business processes.
To harness the power of data effectively, companies should prioritize making data tools more accessible to a broader segment of their workforce. This includes encouraging the use of familiar tools like Microsoft Excel for data analytics, leveraging the proficiency of employees already skilled in these tools.
Python, among programming languages, stands out as a significant driver of AI productivity. Its accessibility and versatility make it the language of choice for a growing community of aspiring data engineers. This aligns with the projection that data science would become an enterprise-wide capability, with Python emerging as the dominant language due to its broad accessibility.
SQL, another essential tool in data management, shares the spotlight with Python as the most popular programming languages today. This combination taps into skills many employees already possess, eliminating the need for extensive retraining and ensuring smoother data operations.
Generative AI (GenAI) represents a promising avenue for empowering employees to work more effectively with data. However, it’s crucial to balance technology with human expertise. While tools like ChatGPT can automate tasks, they sometimes sacrifice technical accuracy for prose quality. Striking the right balance is essential to maintain user trust.
Maximizing data’s role in business growth
The crux of the issue lies not in technology but in how people use it. The NewVantage report consistently highlights that the primary challenges to becoming a data-driven organization are rooted in human factors, such as culture, people, processes, and organizational structures, rather than technical limitations. Despite this recognition, progress in overcoming these challenges has been slow. Often, data executives focus excessively on technological aspects like data modernization, AI, and ML, while neglecting the human dimension.
The key takeaway is that the most valuable asset of any organization is its people, who interpret and utilize data. To successfully transition to a data-driven future, companies must find ways to leverage the existing knowledge and skills of their workforce, making data tools more accessible and aligning technology with human capabilities.
It’s evident that while data investments are on the rise, the success of a data-driven transformation hinges on addressing cultural and human challenges. Rather than relying solely on technology, organizations must prioritize empowering their people to unlock the true potential of data.